Description
As the landscape of scientific research evolves, so do the demands on High-Performance Computing (HPC) centers. Today, integrating Machine Learning (ML) and Artificial Intelligence (AI) services into HPC offerings is becoming increasingly essential. However, this integration comes with its own set of technical challenges and questions that need to be addressed.
Join us for an insightful discussion as we delve into the intersection of High-Performance Computing and Machine Learning, exploring the challenges and opportunities in providing ML/AI services for scientific research. Whether you're a seasoned HPC professional, a data scientist, or an AI enthusiast, this forum promises to offer valuable insights and perspectives from experts in the field. We invite both speakers and attendees to contribute to this important conversation shaping the future of scientific computing.
Key Questions
- Challenges in Offering AI/ML Services:
- What are the unique challenges HPC centers face in providing AI/ML services alongside traditional HPC capabilities?
- How do we ensure scalability and efficiency in AI/ML workflows within HPC infrastructures?
- User Expectations:
- What are the expectations of users regarding AI/ML services within HPC environments?
- How can HPC centers tailor their offerings to meet these expectations effectively?
- Concrete Machine Learning Workflows:
- What are the typical workflows involved in integrating ML/AI with HPC resources?
- How can these workflows be optimized for performance and reliability?
- Tools for AI/ML Services:
- What tools and frameworks are currently available for providing AI/ML services in HPC settings?
- Are there any gaps in tooling that need to be addressed to better support AI/ML integration?
- Data Requirements and Considerations:
- What specific requirements does ML/AI integration impose on data storage, access, and management within HPC centers?
- How can HPC centers navigate the complexities of handling diverse data types and sources?
